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1 From Multi Electrode Arrays to Raster Plots The pioneering work of Hubel and Wiesel based on anatomy and single cell recording on brain visual areas was very useful. However at that time, little was known about the properties of the retinal neural network. Similarly, today, the anatomical description of dierent types of G cells is a well known piece of literature, in contrast to their collective neural response that is partly missing. To overcome limitations of single-electrodes recording and to access to the coding response of a population of neurons, multi-electrodes (MEA) devices are used in physiology. MEA devices are formed by an array of isolated electrodes (64 to 256, separated from 30- 200 microns each). When in contact with a small piece of neural tissue, a MEA is able to record the simultaneous activity (spike and/or eld potential) from, e.g., 10-150 G cells. The nal goal is to produce from the MEA signal a raster plot of G cells activity, namely a graph with time in abscissa and a neuron labeling in ordinate such that a vertical bar is drawn each time a neuron emits a spike. This poses an important challenge for signal processing: to sort out from a complex (spatial and temporal) neural signal superposition recording the contribution of each cell. With the recent increase in the number of electrodes of MEA devices, the necessity of adequate spike sorting algorithms turns out to be critical. Recently the Berry's lab at Princeton has developed an ecient method, enabling to sort out, from a 256 MEA experiment, about 200 dierent G cells (personal communication). MEA devices constitute an excellent tool to track the physiological properties of G cells as well as their coding capacity. Before the introduction of MEA devices, the neural coding properties of single G cells was study using intra or extra cellular electrodes, giving a limited sense of their collective role. In that respect, the work of Markus Meister using MEA devices was pioneer. With simple stimulus, like checkerboard random white noise, and spike sorting algorithms these authors were able to determinate the number of spiking cells and their respective RF. They have shown that concerted G cells are critical, not only for retina development, but for the neural coding processing. 1 Figure 1: (a) Data from neural tissue are collected with a multielectrode array. Here, a retina is placed over the electrodes. Typically, tens to hundreds of neurons are recorded, but tens of thousands of neurons are in the slice. (b) Activity in individual neurons is detected as very short voltage spikes. The time of minimum voltage is marked by a dot. (c) All spikes from all neurons are plotted over time, where a dot represents the time of a spike. (d) The state of an ensemble of ve neurons in one time bin is represented as a vector, where ones indicate spikes and zeros indicate no spikes. Time bins are usually a few milliseconds. (e) The goal of the second-order maximum entropy model is to explain the probability distribution of states found in the data, given only information about the ring rates and pairwise correlations between neurons. Here a schematic probability distribution is shown for data and the model. Neural Networks (Entropy)) 2 (Maximum Entropy Approaches to Living